Abstract
In this article, a multiagent system is proposed to solve Job Shop Scheduling Problems. In the proposed system, a number of autonomous agents cooperate in a Multi-Population Cultural Algorithm (MP-CA) framework. The proposed multiagent system consists of a number of groups of agents called sub-populations. The agents in each sub-population are co-evolving using a local CA. The local CAs are working in parallel and communicating to each other to exchange their extracted knowledge. The knowledge is migrated in the form of structured belief which is defined as a statistical records of an agent or a group of agents. Experiments show that our method outperforms some existing methods by offering better solutions as well as a better convergence rate.
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Raeesi N., M.R., Kobti, Z. (2012). A Multiagent System to Solve JSSP Using a Multi-Population Cultural Algorithm. In: Kosseim, L., Inkpen, D. (eds) Advances in Artificial Intelligence. Canadian AI 2012. Lecture Notes in Computer Science(), vol 7310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30353-1_37
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DOI: https://doi.org/10.1007/978-3-642-30353-1_37
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